'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


SCREAMING_SNAKE_CASE_:	Tuple    =re.compile(r'\b(a|an|the)\b', re.UNICODE)

SCREAMING_SNAKE_CASE_:	Optional[int]    =None





def       lowerCAmelCase_							(   )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0."     )
     parser.add_argument("data_file"   ,       metavar="data.json"   ,       help="Input data JSON file."     )
     parser.add_argument("pred_file"   ,       metavar="pred.json"   ,       help="Model predictions."     )
     parser.add_argument(
         "--out-file"   ,       "-o"   ,       metavar="eval.json"   ,       help="Write accuracy metrics to file (default is stdout)."     )
     parser.add_argument(
         "--na-prob-file"   ,       "-n"   ,       metavar="na_prob.json"   ,       help="Model estimates of probability of no answer."     )
     parser.add_argument(
         "--na-prob-thresh"   ,       "-t"   ,       type=snake_case_   ,       default=1.0   ,       help="Predict \"\" if no-answer probability exceeds this (default = 1.0)."   ,       )
     parser.add_argument(
         "--out-image-dir"   ,       "-p"   ,       metavar="out_images"   ,       default=snake_case_   ,       help="Save precision-recall curves to directory."     )
     parser.add_argument("--verbose"   ,       "-v"   ,       action="store_true"     )
     if len(sys.argv     ) == 1:
          parser.print_help()
          sys.exit(1     )
     return parser.parse_args()





def       lowerCAmelCase_							(   snake_case_			:					List[Any]     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}
     for article in dataset:
          for p in article["paragraphs"]:
               for qa in p["qas"]:
                    UpperCAmelCase_			       =  bool(qa["answers"]["text"]     )
     return qid_to_has_ans





def       lowerCAmelCase_							(   snake_case_			:					int     )       -> Optional[Any]:


     '''simple docstring'''




     def remove_articles(snake_case_			:					Optional[int]     ):
          return ARTICLES_REGEX.sub(" "   ,       snake_case_     )

     def white_space_fix(snake_case_			:					Tuple     ):
          return " ".join(text.split()     )

     def remove_punc(snake_case_			:					Optional[int]     ):
          UpperCAmelCase_			       =  set(string.punctuation     )
          return "".join(ch for ch in text if ch not in exclude     )

     def lower(snake_case_			:					Tuple     ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(snake_case_     )     )     )     )





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]     )       -> Dict:


     '''simple docstring'''




     if not s:
          return []
     return normalize_answer(snake_case_     ).split()





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]   ,       snake_case_			:					Union[str, Any]     )       -> Optional[Any]:


     '''simple docstring'''




     return int(normalize_answer(snake_case_     ) == normalize_answer(snake_case_     )     )





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					Any     )       -> Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_			       =  get_tokens(snake_case_     )
     UpperCAmelCase_			       =  get_tokens(snake_case_     )
     UpperCAmelCase_			       =  collections.Counter(snake_case_     ) & collections.Counter(snake_case_     )
     UpperCAmelCase_			       =  sum(common.values()     )
     if len(snake_case_     ) == 0 or len(snake_case_     ) == 0:
          # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
          return int(gold_toks == pred_toks     )
     if num_same == 0:
          return 0
     UpperCAmelCase_			       =  1.0 * num_same / len(snake_case_     )
     UpperCAmelCase_			       =  1.0 * num_same / len(snake_case_     )
     UpperCAmelCase_			       =  (2 * precision * recall) / (precision + recall)
     return fa





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					Optional[Any]     )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}
     UpperCAmelCase_			       =  {}
     for article in dataset:
          for p in article["paragraphs"]:
               for qa in p["qas"]:
                    UpperCAmelCase_			       =  qa["id"]
                    UpperCAmelCase_			       =  [t for t in qa["answers"]["text"] if normalize_answer(snake_case_     )]
                    if not gold_answers:
                         # For unanswerable questions, only correct answer is empty string
                         UpperCAmelCase_			       =  [""]
                    if qid not in preds:
                         print(f"""Missing prediction for {qid}"""     )
                         continue
                    UpperCAmelCase_			       =  preds[qid]
                    # Take max over all gold answers
                    UpperCAmelCase_			       =  max(compute_exact(snake_case_   ,       snake_case_     ) for a in gold_answers     )
                    UpperCAmelCase_			       =  max(compute_fa(snake_case_   ,       snake_case_     ) for a in gold_answers     )
     return exact_scores, fa_scores





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					List[str]     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  {}
     for qid, s in scores.items():
          UpperCAmelCase_			       =  na_probs[qid] > na_prob_thresh
          if pred_na:
               UpperCAmelCase_			       =  float(not qid_to_has_ans[qid]     )
          else:
               UpperCAmelCase_			       =  s
     return new_scores





def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					Any   ,       snake_case_			:					int=None     )       -> List[Any]:


     '''simple docstring'''




     if not qid_list:
          UpperCAmelCase_			       =  len(snake_case_     )
          return collections.OrderedDict(
              [
                  ("exact", 100.0 * sum(exact_scores.values()     ) / total),
                  ("f1", 100.0 * sum(fa_scores.values()     ) / total),
                  ("total", total),
              ]     )
     else:
          UpperCAmelCase_			       =  len(snake_case_     )
          return collections.OrderedDict(
              [
                  ("exact", 100.0 * sum(exact_scores[k] for k in qid_list     ) / total),
                  ("f1", 100.0 * sum(fa_scores[k] for k in qid_list     ) / total),
                  ("total", total),
              ]     )





def       lowerCAmelCase_							(   snake_case_			:					Tuple   ,       snake_case_			:					Tuple   ,       snake_case_			:					str     )       -> List[str]:


     '''simple docstring'''




     for k in new_eval:
          UpperCAmelCase_			       =  new_eval[k]





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Any   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					Any     )       -> List[str]:


     '''simple docstring'''




     plt.step(snake_case_   ,       snake_case_   ,       color="b"   ,       alpha=0.2   ,       where="post"     )
     plt.fill_between(snake_case_   ,       snake_case_   ,       step="post"   ,       alpha=0.2   ,       color="b"     )
     plt.xlabel("Recall"     )
     plt.ylabel("Precision"     )
     plt.xlim([0.0, 1.05]     )
     plt.ylim([0.0, 1.05]     )
     plt.title(snake_case_     )
     plt.savefig(snake_case_     )
     plt.clf()





def       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					Any   ,       snake_case_			:					str   ,       snake_case_			:					Optional[Any]=None   ,       snake_case_			:					int=None     )       -> Tuple:


     '''simple docstring'''




     UpperCAmelCase_			       =  sorted(snake_case_   ,       key=lambda snake_case_     : na_probs[k]     )
     UpperCAmelCase_			       =  0.0
     UpperCAmelCase_			       =  1.0
     UpperCAmelCase_			       =  0.0
     UpperCAmelCase_			       =  [1.0]
     UpperCAmelCase_			       =  [0.0]
     UpperCAmelCase_			       =  0.0
     for i, qid in enumerate(snake_case_     ):
          if qid_to_has_ans[qid]:
               true_pos += scores[qid]
          UpperCAmelCase_			       =  true_pos / float(i + 1     )
          UpperCAmelCase_			       =  true_pos / float(snake_case_     )
          if i == len(snake_case_     ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
               # i.e., if we can put a threshold after this point
               avg_prec += cur_p * (cur_r - recalls[-1])
               precisions.append(snake_case_     )
               recalls.append(snake_case_     )
     if out_image:
          plot_pr_curve(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )
     return {"ap": 100.0 * avg_prec}





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Dict   ,       snake_case_			:					List[Any]   ,       snake_case_			:					List[Any]   ,       snake_case_			:					Any   ,       snake_case_			:					Dict     )       -> Union[str, Any]:


     '''simple docstring'''




     if out_image_dir and not os.path.exists(snake_case_     ):
          os.makedirs(snake_case_     )
     UpperCAmelCase_			       =  sum(1 for v in qid_to_has_ans.values() if v     )
     if num_true_pos == 0:
          return
     UpperCAmelCase_			       =  make_precision_recall_eval(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       out_image=os.path.join(snake_case_   ,       "pr_exact.png"     )   ,       title="Precision-Recall curve for Exact Match score"   ,       )
     UpperCAmelCase_			       =  make_precision_recall_eval(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       out_image=os.path.join(snake_case_   ,       "pr_f1.png"     )   ,       title="Precision-Recall curve for F1 score"   ,       )
     UpperCAmelCase_			       =  {k: float(snake_case_     ) for k, v in qid_to_has_ans.items()}
     UpperCAmelCase_			       =  make_precision_recall_eval(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       out_image=os.path.join(snake_case_   ,       "pr_oracle.png"     )   ,       title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"   ,       )
     merge_eval(snake_case_   ,       snake_case_   ,       "pr_exact"     )
     merge_eval(snake_case_   ,       snake_case_   ,       "pr_f1"     )
     merge_eval(snake_case_   ,       snake_case_   ,       "pr_oracle"     )





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					List[Any]   ,       snake_case_			:					Tuple   ,       snake_case_			:					Union[str, Any]     )       -> List[Any]:


     '''simple docstring'''




     if not qid_list:
          return
     UpperCAmelCase_			       =  [na_probs[k] for k in qid_list]
     UpperCAmelCase_			       =  np.ones_like(snake_case_     ) / float(len(snake_case_     )     )
     plt.hist(snake_case_   ,       weights=snake_case_   ,       bins=20   ,       range=(0.0, 1.0)     )
     plt.xlabel("Model probability of no-answer"     )
     plt.ylabel("Proportion of dataset"     )
     plt.title(f"""Histogram of no-answer probability: {name}"""     )
     plt.savefig(os.path.join(snake_case_   ,       f"""na_prob_hist_{name}.png"""     )     )
     plt.clf()





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					Tuple   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					List[str]     )       -> Any:


     '''simple docstring'''




     UpperCAmelCase_			       =  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]     )
     UpperCAmelCase_			       =  num_no_ans
     UpperCAmelCase_			       =  cur_score
     UpperCAmelCase_			       =  0.0
     UpperCAmelCase_			       =  sorted(snake_case_   ,       key=lambda snake_case_     : na_probs[k]     )
     for i, qid in enumerate(snake_case_     ):
          if qid not in scores:
               continue
          if qid_to_has_ans[qid]:
               UpperCAmelCase_			       =  scores[qid]
          else:
               if preds[qid]:
                    UpperCAmelCase_			       =  -1
               else:
                    UpperCAmelCase_			       =  0
          cur_score += diff
          if cur_score > best_score:
               UpperCAmelCase_			       =  cur_score
               UpperCAmelCase_			       =  na_probs[qid]
     return 100.0 * best_score / len(snake_case_     ), best_thresh





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					Tuple   ,       snake_case_			:					Any   ,       snake_case_			:					Optional[int]   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					str     )       -> Tuple:


     '''simple docstring'''




     UpperCAmelCase_       ,					UpperCAmelCase_			       =  find_best_thresh(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )
     UpperCAmelCase_       ,					UpperCAmelCase_			       =  find_best_thresh(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )
     UpperCAmelCase_			       =  best_exact
     UpperCAmelCase_			       =  exact_thresh
     UpperCAmelCase_			       =  best_fa
     UpperCAmelCase_			       =  fa_thresh





def       lowerCAmelCase_							(   )       -> List[str]:


     '''simple docstring'''




     with open(OPTS.data_file     ) as f:
          UpperCAmelCase_			       =  json.load(snake_case_     )
          UpperCAmelCase_			       =  dataset_json["data"]
     with open(OPTS.pred_file     ) as f:
          UpperCAmelCase_			       =  json.load(snake_case_     )
     if OPTS.na_prob_file:
          with open(OPTS.na_prob_file     ) as f:
               UpperCAmelCase_			       =  json.load(snake_case_     )
     else:
          UpperCAmelCase_			       =  {k: 0.0 for k in preds}
     UpperCAmelCase_			       =  make_qid_to_has_ans(snake_case_     )  # maps qid to True/False
     UpperCAmelCase_			       =  [k for k, v in qid_to_has_ans.items() if v]
     UpperCAmelCase_			       =  [k for k, v in qid_to_has_ans.items() if not v]
     UpperCAmelCase_       ,					UpperCAmelCase_			       =  get_raw_scores(snake_case_   ,       snake_case_     )
     UpperCAmelCase_			       =  apply_no_ans_threshold(snake_case_   ,       snake_case_   ,       snake_case_   ,       OPTS.na_prob_thresh     )
     UpperCAmelCase_			       =  apply_no_ans_threshold(snake_case_   ,       snake_case_   ,       snake_case_   ,       OPTS.na_prob_thresh     )
     UpperCAmelCase_			       =  make_eval_dict(snake_case_   ,       snake_case_     )
     if has_ans_qids:
          UpperCAmelCase_			       =  make_eval_dict(snake_case_   ,       snake_case_   ,       qid_list=snake_case_     )
          merge_eval(snake_case_   ,       snake_case_   ,       "HasAns"     )
     if no_ans_qids:
          UpperCAmelCase_			       =  make_eval_dict(snake_case_   ,       snake_case_   ,       qid_list=snake_case_     )
          merge_eval(snake_case_   ,       snake_case_   ,       "NoAns"     )
     if OPTS.na_prob_file:
          find_all_best_thresh(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )
     if OPTS.na_prob_file and OPTS.out_image_dir:
          run_precision_recall_analysis(snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       OPTS.out_image_dir     )
          histogram_na_prob(snake_case_   ,       snake_case_   ,       OPTS.out_image_dir   ,       "hasAns"     )
          histogram_na_prob(snake_case_   ,       snake_case_   ,       OPTS.out_image_dir   ,       "noAns"     )
     if OPTS.out_file:
          with open(OPTS.out_file   ,       "w"     ) as f:
               json.dump(snake_case_   ,       snake_case_     )
     else:
          print(json.dumps(snake_case_   ,       indent=2     )     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	List[str]    =parse_args()
    if OPTS.out_image_dir:
        import matplotlib

        matplotlib.use('Agg')
        import matplotlib.pyplot as plt
    main()









'''simple docstring'''






def  lowerCamelCase__			(		_A					,     _A					,     _A=False							):

  if isinstance(_A					,     _A							) and isinstance(_A					,     _A							):
    a		:   Tuple	  =	len(set_a.intersection(_A							)							)

    if alternative_union:
      a		:   Union[str, Any]	  =	len(_A							) + len(_A							)
    else:
      a		:   Optional[Any]	  =	len(set_a.union(_A							)							)

    return intersection / union

  if isinstance(_A					,     (list, tuple)							) and isinstance(_A					,     (list, tuple)							):
    a		:   int	  =	[element for element in set_a if element in set_b]

    if alternative_union:
      a		:   Optional[int]	  =	len(_A							) + len(_A							)
      return len(_A							) / union
    else:
      a		:   Tuple	  =	set_a + [element for element in set_b if element not in set_a]
      return len(_A							) / len(_A							)

    return len(_A							) / len(_A							)
  return None


if __name__ == "__main__":
     lowerCAmelCase:   str					=		{'a', 'b', 'c', 'd', 'e'}
     lowerCAmelCase:   Optional[Any]					=		{'c', 'd', 'e', 'f', 'h', 'i'}
     print(jaccard_similarity(set_a, set_b))